Predictive analytics generally refers to techniques for extracting information from data to build a model that can predict an output from a given input. Predicting an output can include predicting future trends or behavior patterns or performing sentiment analysis, to name a few examples. Various types of predictive models can be used to analyze data and generate predictive outputs. Typically, a predictive model is trained with training data that includes input data and output data that mirror the form of input data that will be entered into the predictive model and the desired predictive output, respectively.
Computer systems frequently have multiple different types of memory that operate at different speeds. Primary storage modules (for example, Dynamic-Random Access Memory (DRAM) modules) are fast to access but are relatively expensive. In contrast, secondary storage modules (for example, hard drives) are inexpensive but slower to access. Generally, computer systems read programs and data from secondary storage into primary storage before executing the program.